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Attention mechanism diagram For unet Deep Learning

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Dear all,
Anyone know how to add the Attention mechanism diagram using deep network design Matlab?

Accepted Answer

Aditya
Aditya on 17 Oct 2023
Hi Akmal
I understand that you want help in adding the attention mechanism diagram using deep network design MATLAB.Here's an example of how you can add an attention mechanism to your deep learning model using the Layer API:
% Define the attention layer
attentionLayer = attentionLayer('AttentionSize', attentionSize);
% Create the rest of your deep learning model
layers = [
imageInputLayer([inputImageSize])
convolution2dLayer(3, 64, 'Padding', 'same')
reluLayer
attentionLayer
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer
];
% Create the deep learning network
net = layerGraph(layers);
% Visualize the network
plot(net);
In this example, the `attentionLayer` is manually defined using the `attentionLayer` function from the Layer API. The `AttentionSize` parameter specifies the size of the attention mechanism.
You can then create the rest of your deep learning model using the Layer API, including other layers such as convolutional layers, fully connected layers, and output layers.
Finally, you can create the deep learning network using the `layerGraph` function and visualize it using the `plot` function.
Please note that the specific implementation of the attention mechanism may vary depending on your requirements and the architecture of your deep learning model. You can customize the attention layer further based on your specific needs.
If you need more advanced or specialized attention mechanisms, you may need to implement them manually using custom layers or explore external deep learning libraries or frameworks that provide built-in support for attention mechanisms.
Hope this helps.
  1 Comment
mohd akmal masud
mohd akmal masud on 20 Oct 2023
Dear @Aditya
If below is my coding for 3D U-Net, The how to put the attention mechanism ?
clc
clear all
close all
%testDataimages
DATASetDir = fullfile('C:\Users\USER\Downloads\HEAD & NECK\HEAD & NECK');
IMAGEDir = fullfile(DATASetDir,'ImagesTr');
volReader = @(x) matRead(x);
volds = imageDatastore(IMAGEDir, ...
'FileExtensions','.mat','ReadFcn',volReader);
% labelReader = @(x) matread(x);
matFileDir = fullfile('C:\Users\USER\Downloads\HEAD & NECK\HEAD & NECK\LabelsTr');
classNames = ["background", "tumor"];
pixelLabelID = [0 1];
% pxds = (LabelDirr,classNames,pixelLabelID, ...
% 'FileExtensions','.mat','ReadFcn',labelReader);
pxds = pixelLabelDatastore(matFileDir,classNames,pixelLabelID, ...
'FileExtensions','.mat','ReadFcn',@matRead);
volume = preview(volds);
label = preview(pxds);
up1 = uipanel;
h = labelvolshow(label, volume, 'Parent', up1);
h.CameraPosition = [4 2 -3.5];
h.LabelVisibility(1) = 0;
h.VolumeThreshold = 0.5;
volumeViewer(volume, label)
patchSize = [128 128 36];
patchPerImage = 16;
miniBatchSize = 8;
patchds = randomPatchExtractionDatastore(volds,pxds,patchSize, ...
'PatchesPerImage',patchPerImage);
patchds.MiniBatchSize = miniBatchSize;
dsTrain = transform(patchds,@augment3dPatch);
volLocVal = fullfile('C:\Users\USER\Downloads\HEAD & NECK\HEAD & NECK\ImagesVal');
voldsVal = imageDatastore(volLocVal, ...
'FileExtensions','.mat','ReadFcn',volReader);
lblLocVal = fullfile('C:\Users\USER\Downloads\HEAD & NECK\HEAD & NECK\LabelsVal');
pxdsVal = pixelLabelDatastore(lblLocVal,classNames,pixelLabelID, ...
'FileExtensions','.mat','ReadFcn',volReader);
dsVal = randomPatchExtractionDatastore(voldsVal,pxdsVal,patchSize, ...
'PatchesPerImage',patchPerImage);
dsVal.MiniBatchSize = miniBatchSize;
inputSize = [128 128 36];
numClasses = 2;
encoderDepth = 2;
lgraph = unet3dLayers(inputSize,numClasses,'EncoderDepth',encoderDepth,'NumFirstEncoderFilters',16)
figure,plot(lgraph);
%analyzeNetwork(lgraph1)
%analyzeNetwork(lgraph2)
% maxEpochs = 100;
% options = trainingOptions('adam', ...
% 'MaxEpochs',maxEpochs, ...
% 'InitialLearnRate',1e-3, ...
% 'LearnRateSchedule','piecewise', ...
% 'LearnRateDropPeriod',5, ...
% 'LearnRateDropFactor',0.97, ...
% 'ValidationData',dsVal, ...
% 'ValidationFrequency',200, ...
% 'Plots','training-progress', ...
% 'Verbose',false, ...
% 'MiniBatchSize',miniBatchSize);
options = trainingOptions('sgdm', ...
'MiniBatchSize',1, ...
'MaxEpochs',100, ...
'InitialLearnRate',1e-3, ...
'Shuffle','every-epoch', ...
'ValidationData',dsVal, ...
'ValidationFrequency',200, ...
'Verbose',false, ...
'Plots','training-progress', ...
'ExecutionEnvironment','cpu');
doTraining = true;
if doTraining
modelDateTime = datestr(now,'dd-mmm-yyyy-HH-MM-SS');
[net,info] = trainNetwork(dsTrain,lgraph,options);
save(['trained3DUNet-' modelDateTime '-Epoch-' num2str(maxEpochs) '.mat'],'net');
else
load('trained3DVNet-07-Jun-2022-13-45-30-Epoch-250.mat');
end

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More Answers (1)

Gobert
Gobert on 27 May 2024
The simplest way is to create a custom layer function (addAttentionLayer) that modifies the layers of the unet3dLayers function, allowing you to insert your attentionLayer.
For example:
lgraph = unet3dLayers(inputSize,numClasses,'EncoderDepth',encoderDepth,'NumFirstEncoderFilters',16);
lgraph = addAttentionLayer(lgraph);
I hope this helps!

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